Facenet: How to finetine pretrained model on my own dataset of 43 persons?

[Victoria2333]

@davidsandberg I have been recently using this pretrained model to train my few-shot dataset of 43 persons, but i always got the error of classes not matching. maybe because my dataset size is 43 while MS-Celeb size is 10575. I'm not familiar with the saving and restoring models in TF, I want to restore part of the parameters and to finetune on my small dataset. So would you please give me some instructions about your finetune step??
Thank you~~~

[speculaas]

Hi Vic,

I had this question too.

There is answer in previous issue discussions:
github.com/davidsandberg/facenet/issues/139

The above issue#139 covers how to restore.
Then how to specify which layer to fine tune : (ps.1)

1. see "train_softmax.py" , there is a line : "train_op = facenet.train(", and this is where you put in whatever layers you want to fine tune

2. next, to specify whatever layers you want to fine tune : for example :
   ftune_vlist = [v for v in all_vars if v.name.startswith('InceptionResnetV1/Block8')]

3. pass ftune_vlist to facenet.train

for more details : David's wiki : https://github.com/davidsandberg/facenet/wiki/Classifier-training-of-inception-resnet-v1

ps.1 : maybe you need to see tensorboard or some source code to see names for different layers

BR,
JimmyYS

[Victoria2333]

@speculaas, Hi , speculaas, I've been working on the influence of finetune for face recognition. Here is my code:
all_vars = tf.trainable_variables()
set_A_vars = [v for v in all_vars if v.name.startswith('InceptionResnetV1/Block8')]
saver_set_A = tf.train.Saver(set_A_vars, max_to_keep=3)
saver_set_A_and_B = tf.train.Saver(all_vars, max_to_keep=3)

Then I do the transfer learning by restoring the variables in set A:
saver_set_A.restore(sess, pretrained_model)

And save trained model completely:
save_variables_and_metagraph(sess, saver_set_A_and_B, summary_writer, model_dir, subdir, epoch)

I want to test if the finetune of higher layers may do good to accuracy, but my accuracy on small dataset is very low , about o.318, so I want to know about your progress about this??? Did you find the progress of finetuning higher layers??

[anjiang2016]

1. see "train_softmax.py" , find codes like below:
   #Build a Graph that trains the model with one batch of examples and updates the model parameters
   train_op = facenet.train(total_loss, global_step, args.optimizer,
   learning_rate, args.moving_average_decay, tf.global_variables(), args.log_histograms)
   #Create a saver
   saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)

2. replace it by below:
   #fine_turn ftune_vlist variables
   all_vars = tf.trainable_variables()
   ftune_vlist = [v for v in all_vars if v.name.startswith('InceptionResnetV1/Block8')]
   train_op = facenet.train(total_loss, global_step, args.optimizer,
   learning_rate, args.moving_average_decay, ftune_vlist, args.log_histograms)
   #Create a saver
   saver = tf.train.Saver(ftune_vlist, max_to_keep=3)

3. and then u will just tune the layer in ftun_vlist's values.

[hsm4703]

fine_turn ftune_vlist variables after training performance is better ?

[hsm4703]

i use the

1. see "train_softmax.py" , find codes like below:
   #Build a Graph that trains the model with one batch of examples and updates the model parameters
   train_op = facenet.train(total_loss, global_step, args.optimizer,
   learning_rate, args.moving_average_decay, tf.global_variables(), args.log_histograms)
   #Create a saver
   saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
2. replace it by below:
   #fine_turn ftune_vlist variables
   all_vars = tf.trainable_variables()
   ftune_vlist = [v for v in all_vars if v.name.startswith('InceptionResnetV1/Block8')]
   train_op = facenet.train(total_loss, global_step, args.optimizer,
   learning_rate, args.moving_average_decay, ftune_vlist, args.log_histograms)
   #Create a saver
   saver = tf.train.Saver(ftune_vlist, max_to_keep=3)
3. and then u will just tune the layer in ftun_vlist's values.

i use code find Similar to the previous program

[keshavshrikant]

1. see "train_softmax.py" , find codes like below:
   #Build a Graph that trains the model with one batch of examples and updates the model parameters
   train_op = facenet.train(total_loss, global_step, args.optimizer,
   learning_rate, args.moving_average_decay, tf.global_variables(), args.log_histograms)
   #Create a saver
   saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
2. replace it by below:
   #fine_turn ftune_vlist variables
   all_vars = tf.trainable_variables()
   ftune_vlist = [v for v in all_vars if v.name.startswith('InceptionResnetV1/Block8')]
   train_op = facenet.train(total_loss, global_step, args.optimizer,
   learning_rate, args.moving_average_decay, ftune_vlist, args.log_histograms)
   #Create a saver
   saver = tf.train.Saver(ftune_vlist, max_to_keep=3)
3. and then u will just tune the layer in ftun_vlist's values.

How do I restore the model for validation in this case?